sw-SVM: sensor weighting support vector machines for EEG-based brain-computer interfaces
sw-SVM: sensor weighting support vector machines for EEG-based brain-computer interfaces
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DOI：
10.1088/1741-2560/8/5/056004
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发表时间：
2011-10-01
影响因子：
4
通讯作者：
Rakotomamonjy, A.
中科院分区：
文献类型：
--
作者：
Jrad, N.;Congedo, M.;Rakotomamonjy, A.
在许多机器学习应用中，如脑机接口（BCI），高维传感器阵列数据可用。传感器测量通常是高度相关的，并且信噪比在传感器之间不是均匀分布的。因此，收集的数据是高度可变的，辨别任务是具有挑战性的。在这项工作中，我们专注于传感器加权作为一种有效的工具，以提高分类过程。我们提出了一种方法集成传感器加权的分类框架。传感器权重被认为是超参数，由支持向量机（SVM）学习。由此产生的传感器加权SVM（SW-SVM）的设计，以满足一个裕度标准，即，泛化误差。两个数据集上的实验研究，P300数据集和错误相关电位（ErrP）数据集。对于P300数据集（BCI竞争III），其中大量的试验是可用的，SW-SVM证明执行等效相对于集成SVM策略，赢得了竞争。对于ErrP数据集，其中少量的试验是可用的，SW-SVM显示出上级性能相比，三个国家的最先进的方法。结果表明，SW-SVM有望在事件相关电位分类中是有用的，即使有少量的训练试验。
In many machine learning applications, like brain-computer interfaces (BCI), high-dimensional sensor array data are available. Sensor measurements are often highly correlated and signal-to-noise ratio is not homogeneously spread across sensors. Thus, collected data are highly variable and discrimination tasks are challenging. In this work, we focus on sensor weighting as an efficient tool to improve the classification procedure. We present an approach integrating sensor weighting in the classification framework. Sensor weights are considered as hyper-parameters to be learned by a support vector machine (SVM). The resulting sensor weighting SVM (sw-SVM) is designed to satisfy a margin criterion, that is, the generalization error. Experimental studies on two data sets are presented, a P300 data set and an error-related potential (ErrP) data set. For the P300 data set (BCI competition III), for which a large number of trials is available, the sw-SVM proves to perform equivalently with respect to the ensemble SVM strategy that won the competition. For the ErrP data set, for which a small number of trials are available, the sw-SVM shows superior performances as compared to three state-of-the art approaches. Results suggest that the sw-SVM promises to be useful in event-related potentials classification, even with a small number of training trials.